Prefatory Note: The Origins of "The Agorics Project"

by Don Lavoie

Readers of this journal are accustomed to cross-disciplinary explorations
from economics to a number of other sciences, but until now, to my knowledge,
there have not been any attempts to communicate with the field of computer science.
In September of 1989 at George Mason University there began something that is
being called the "Agorics Project" in which graduate students from
the Artificial Intelligence Laboratory at GMU's Department of Computer Science
joined several economists at the Market Processes Center to investigate a number
of topics of mutual interest. The name "agorics" is borrowed from
some research that moves in the opposite direction across that disciplinary
boundary, trying to use economics in computer science, but the aim of our group
is to explore some ways that economics might benefit from looking into certain
developments in computer science.

The accompanying article is the product of several months of conversations
among computer scientists and market process economists who have begun to delve
into the five distinct research areas the article describes. The substance of
the topics we have been discussing is summarized there, but in this note I would
like to supply a bit of historical background that might help to explain how
it happened that several of us here at the Market Processes Center have suddenly
found ourselves looking into these new research areas. I will tell the story
in a rather autobiographical manner, not so much to try to claim all the credit
for bringing computer science issues to the attention of market process economists
as to try to share the blame. Although I had always wanted to find a way to
take advantage of my background in computer science, it was only as a result
of numerous discussions I have had with the various individuals I will be mentioning
that I finally decided to return to the computer field. Only time will tell
what gains may result from this sort of intellectual interchange.

Computers are my first love. The field of computer science in which I have
been the most interested is artificial intelligence. When I was first introduced
to computers while still a high-school student, I was trying to design a program
to play poker. As an undergraduate majoring in computer science at Worcester
Polytechnic Institute, I did a senior project with two other students that produced
a program to "simulate" the process of music composition. Based on
discrete event techniques, the program wrote "music" step by step
according to the rules of the Baroque fugue. I presented the results with my
co-authors at a professional meeting of computer scientists, who seemed to like
it, but we were acutely aware that if it were a meeting of musicians it would
have not been such a hit. Whether trying to play card games or compose music
on computers, the experience of trying to simulate what human minds do was humbling.
There is probably no better way to realize the serious limitations of computers
and the amazing subtleties of human intelligence than to attempt to reproduce
the thinking process. It was easy to get the programs to mechanically obey specific
rules, but it was something else to try to replicate Bach.

While at college, I first became interested in economics. Ever since, I have
wanted to find a way to legitimately use computers in economics. My attraction
to the market process school came from the way it addresses the questions about
the nature of human knowledge which I had been thinking about from the standpoint
of artificial intelligence. Most of the ways computers have been used by economists
have not interested me in the slightest, because of the way they treat action
as purely mechanical. But I was not convinced that computers were necessarily
"mechanistic" in the relevant sense. I began to explore the possibility
of designing a non-mechanistic kind of simulation with which market process
economics could be comfortable. My exposure to discrete event simulation techniques
as an undergraduate suggested to me that such aspects of choice as creativity
and divergent perceptions, which have not been modeled well by mainstream economics,
might be "simulatable" with such methods. It might be possible to
construct computer simulations that could be used as mental experiments for
developing market process theory.

When I began graduate school in economics at New York University I continued
to think about the possibility of such computer simulations, and intended to
write my dissertation on the topic. The seminar I gave on the idea to the weekly
Austrian colloquium at NYU was not exactly met with enthusiasm, but it was greeted
with enough openness and tolerance to encourage me to keep thinking about it.
I continued working on it for about a year, getting so far as to design the
main structure of the program. Eventually, however, I talked myself out of the
project. I was sufficiently concerned that I would get nothing useful out of
it that I decided not to risk my Ph.D. on it, and chose a less risky topic in
the history of thought. (The first economics paper I had written was for a computer
science class arguing, rather unpersuasively, why computers could not answer
Mises's 1920 challenge to socialism, and I had always wanted to make that case
more complete.) So until recently, computer programming has been merely the
way I worked myself through graduate school, and has had nothing to do with
my economics research.

A few years later, as a more secure, soon-to-be-tenured faculty member at
GMU, I began thinking about the idea again. I started discussing it with a graduate
student, Ralph Rector, who also had computer programming experience, and who
took an immediate interest. We had regular sessions for a couple of months,
exploring some of the possibilities as well as the apparent obstacles and methodological
objections. As a result of these discussions my thinking advanced considerably
about how such simulations might be constructed. For the first time, I had found
someone to talk with at length about the kind of project I had in mind, and
I was reinforced in my belief that in principle it might be feasible. Ralph
developed some of the main components of an algorithm for decision-making under
radical uncertainty, and our discussions helped to resolve several of the conceptual
difficulties I had originally worried about. Yet I still had serious doubts,
and never really turned my research back in the direction of computer science.
Although Ralph had not really given up, I ended up talking him out of the simulation
project on the grounds that it was too high a risk for a dissertation topic.

Now I am rejuvenating the idea for a third time. Above all I have one of my
graduate students, Bill Tulloh, along with two computer scientists from Palo
Alto, Mark Miller and Eric Drexler, to thank for getting me back into computer
science in general, and the simulation idea in particular. Bill became interested
in the Miller and Drexler articles as a result of his explorations of recent
developments in the study of complex phenomena and self-ordering systems, an
interest stemming from a paper he wrote for Jack High's graduate course in market
process economics.

Bill alerted me to two path-breaking papers by Mil1er and Drexler, which used
Hayekian ideas to attack certain classic problems within computer science. The
understanding these software engineers exhibited of Hayek seemed to me to be
far more sophisticated than that of most economists, and the number of ways
in which contemporary computer science appeared to be closely related to Hayekians
themes was astonishing. Bill also introduced me to dozens of interesting articles
in Artificial Intelligence and other areas, which renewed my interest in my
first field of scholarship. He also introduced me to Bob Crosby, secretary of
the Washington Evolutionary Systems Society, whose networks of contacts and
enthusiasm for our work has helped to further fuel this interest.

A group of graduate students at the Center, including Ralph Rector, as well
as Howard Baetjer, Fred Foldvary and Kevin Lacobie, were also impressed by Miller
and Drexler's work. I wrote an enthusiastic letter to the co-authors, who responded
in kind, and suggested that a few of us come out to Silicon Valley to visit
them. They also introduced us to a third person, Phil Salin (see his article
on "The Ecology of Decisions" elsewhere in this issue of Market
Process). Salin had introduced Miller and Drexler to Hayek. I began a series
of lengthy phone conversations with the three of them, and decided to take them
up on their offer to visit them so that we could talk over the simulation idea,
as well as several other possible research topics their papers hinted at. It
turns out that Ralph was already far along on another dissertation topic and
unable to join us, but I was able to bring Bill Tulloh and Howie Baetjer with
me. The trip was co-sponsored by the Market Process Center and the three hosts,
Miller, Drexler, and Salin. The trip was, in our judgment, a resounding success.
Indeed, I think I can say that Howie, Bill and I had one of the most intellectually
stimulating experiences of our lives. The accompanying paper is a product of
that stimulation. The literature the paper surveys was mostly unearthed by Bill,
who almost single-handedly wrote the bibliographic appendix and collected the
references. The main body of the text was mostly put together by Howie and myself.
The underlying ideas, as we point out, are mostly those of our gracious hosts,
as reinterpreted in our own words.

Although as a result of our remarkable visit I have added several new research
areas to my interest in the computer idea, they have not supplanted but only
reinforced that original interest. The last of the five possible research topics
described in the co-authored article sums up this idea of using computer simulations
as mental experiments for economic theory. Although building such simulations
for the purpose of doing economic theory was certainly not the point of the
Miller and Drexler papers, their work has obvious implications for this kind
of research, and it has, for me, put a whole new slant on the simulation idea.
Many of the reasons that had twice dissuaded me from attempting to develop the
simulations no longer seem so daunting. For one thing, Artificial Intelligence
(AI) research has matured a great deal since the days I studied it in college,
and the techniques being used now in machine learning seem much more promising
than anything I had known about in my earlier flirtations with the simulation
idea. Conversations I have had with Mark Miller after our trip have convinced
me that it is an idea worth exploring more seriously.

So we have begun to do just that. The Agorics Project, an informal discussion
group that is looking into ways computer science might be useful to market process
economics, was launched at the Center for the Study of Market Processes in the
fall 1989 semester. The team has so far been composed of Howard Baetjer, David
Burns, Kevin Lacobie, Kurt Schuler, Bill Tulloh, and myself, of the GMU Economics
Department, and Hugo de Garis and Pawel Stefanski, of the Department of Computer
Science. We have had on our discussion agenda all five of the topics mentioned
in the co-authored article, though so far the one we have talked about the most
is the simulation idea. Two distinct programming simulation projects aimed at
the replication of a Mengerian process for the evolution of money have been
initiated by the team, one by de Garis and myself, and one by Stefanski and
Baetjer. Although these simulations are still at a rather rudimentary stage,
we have been encouraged by the results--and even more by the process. The process
of trying to design programs that articulate the logical structure of our economic
theories has been a wonderfully educational experience. We don't feel so much
that we are learning computer science, although that must be happening too,
but that we are learning Mengerian economics.

The group is continuing to meet on an informal basis throughout the spring
1990 semester and this summer, and in the spring of 1991 I will be teaching
a new special topics graduate course on "Economics and Computer Science,"
in the hopes of investigating the various topics more systematically. We welcome
any suggestions our readers may have as we begin to explore the possible gains
from intellectual exchange in this new form of interdisciplinary research.

High-Tech Hayekians:
Some Possible Research Topics in the Economics of Computation

by Don Lavoie, Howard Baetjer and William Tulloh

In a thoroughly intriguing set of papers recently published in a book edited
by B. A. Huberman entitled The Ecology of Computation, two computer scientists,
Mark S. Miller and K. Eric Drexler, have made major advances in what might be
called the Economics of Computation, a new, largely unexplored subdiscipline
on the interface of economics and computer science. What makes this research
especially interesting to several of us at the Center for the Study of Market
Processes is that the kind of economics Miller and Drexler are using to overcome
their own problems in computer science is "our kind." This was no
accident. It turns out that Miller and Drexler were specifically alerted to
the economic writings of Hayek by Phil Salin. Reading Miller and Drexler's papers,
we were struck by the depth of understanding these computer scientists have
of our literature, especially the work on property rights economics and the
spontaneous order theory of F. A. Hayek. The success of these pioneering papers
suggests that there may be some interesting research possibilities in computer
science for the application of market process ideas.

In August of 1989, the three of us made a trip to Palo Alto to visit Miller
and Drexler at the Xanadu Operating Company and the American Information Exchange
Corporation, or "AMIX, " the two software design companies where Miller
and Salin work, and for whom Drexler is a consultant. We were extremely impressed.
Our time in Palo Alto was so replete with ideas, discussion and new insights
that we can hardly begin to summarize them all. We came hoping to find one or
two possible research topics. We returned with too many possible topics for
the three of us to handle. Throughout the visit the conversations always involved
Hayekian ideas, but at the same time they almost always involved computer science.
Miller, Drexler, Salin, et al. consider market process ideas to be of
enormous practical usefulness in the design of commercial software. In this
note we would like to introduce the reader to the most important ideas we encountered,
and indicate why we think market process economists should look into this fascinating
work.

In all, we spoke to some twenty to thirty young computer scientists who are
energetically applying market process insights to some very ambitious software
ventures. They are not primarily academics, but they have academic interests.
These academic interests, however, are focused on practical, real-world applications.
How shall we describe this group? Each of them seems to be a rich brew of scientist,
entrepreneur, programmer and philosopher. Each has a firm understanding of market
principles and a deep appreciation for the general social principle of non-intervention,
for reasons to be described shortly. The organizations they are involved in
are start-up companies that are developing software to help people manage information
in ways that enable knowledge to evolve more rapidly. We visited them in their
homes and in their offices. The animation of our discussions rarely slackened,
even in the cars on the way to restaurants.

The
three people most intensely involved with market process ideas are as follows:

Mark S. Miller--software engineer formerly with Xerox Palo Alto Research Center,
flowing fountain of ideas and enthusiasm, co-author of four of the essays in
the Huberman book, and now chief architect for Xanadu. Mark has derived many
insights about how to design computer programs from studying Hayek.

K. Eric Drexler--formerly with the artificial intelligence laboratory
at MIT, now with the Department of Computer Science at Stanford University,
and author of Engines of Creation, the Coming Era of Nanotechnology,
an investigation of the nature and implications of startling advances in
technology toward which we are moving. Nanotechnology refers to the next step
beyond microtechnology, the ability to achieve technological control over matter
at the molecular level. With serious interests ranging from molecular engineering
and nanomachinery to Austrian economics, Drexler defies categorization in customary
terms. He might best be described as a 1990's version of a Renaissance thinker.
He assists with the Xanadu design work, and with his wife Chris Peterson runs
the Foresight Institute, an organization devoted to preparing the world for
nanotechnology.

Phil
Salin--an entrepreneur, formerly in the telecommunications and space transportation
industries and now on Xanadu's board of directors. He came to see the importance
of Hayek's thought for understanding the business world. He was instrumental
in bringing the Xanadu group to Palo Alto.

We also got the chance to visit with Ted Kaehler, a software engineer with
Apple Computer Corporation and an expert on the field of Artificial Intelligence
known as "machine learning," especially the approach called "neural
networks." Ted is very interested in using market principles in the development
of AI systems.

We also spoke with Marc Stiegler, vice president of Software Engineering at
Xanadu and a professional writer of science fiction. He brings to Xanadu a constant
reminder of the practical steps that need to be taken on a daily basis to reach
the company's ambitious goals. We spoke to many other programmers on the Xanadu
and AMIX teams. All shared an expectant outlook on the future, for computer
science and for society. And yet these individuals are software engineers focused
on practical concerns. They are not just speculating, but busy with the details
of building systems that are immensely practical, however astounding they may
be.

These
individuals are coauthors with us, in a sense, of this paper, since the whole
is a summary of our discussions with them. The development of the research ideas
outlined below was very much a team effort. As usual with such efforts, the
total output exceeds the sum of the inputs. We are grateful to have been on
the team.

It would be useful to distinguish five different kinds of research areas that
our discussions hit upon, within each of which we found several specific possible
projects. Any given project may easily overlap these research areas, but it
will help to keep them distinct, separating them by what each takes as its primary
focus.

1. Process-Oriented Case-Studies of the Computer Industry

As with most media from which things are built, whether the thing is
a cathedral, a bacterium, a sonnet, a fugue or a word processor, architecture
dominates material. To understand clay is not to understand the pot. What
a pot is all about can be appreciated better by understanding the creators
and users of the pot and their need both to inform the material with meaning
and to extract meaning from the form.

There is a qualitative difference between the computer as a medium of
expression and clay or paper. Like the genetic apparatus of a living cell,
the computer can read, write and follow its own markings to levels of self-interpretation
whose intellectual limits are still not understood. Hence the task for someone
who wants to understand software is not simply to see the pot instead of the
clay. It is to see in pots thrown by beginners (for all are beginners in the
fledgling profession of computer science) the possibility of the Chinese porcelain
and Limoges to come.

--Alan Kay (1984, p. 53)

One type of research project which frequently came up was simply to use the
tools of market process economics to examine the specifics of the computer industry.
Such studies would be in keeping with the overall thrust of the research combining
theoretical and empirical study that has been going forward at the Market Processes
Center over the last several years. Not the least important reason economics
might study computation is that a growing and increasingly important part of
the economy consists of the computer industry.

One example of the kind of topic we have in mind here is considering software
as capital. It is a cliche that we are entering the information age, and much
of the capital "equipment" that counts today is in the form of instructions
to computers. Software is a special kind of capital good with some economically
interesting characteristics. Market process economics, since it concentrates
on the way knowledge is used in society, may in fact find this industry especially
intriguing. Increasingly computers play a central role in the process of knowledge
transmission.

Studying
software as capital may help us illuminate the market process approach to capital
theory. Few kinds of tools today are more important than software, for software
increasingly directs our "hard" tools. But software does not fit the
assumptions made about capital goods in mainstream theory. It is reproducible
at negligible cost; its use by one agent does not preclude its use by another;
the major costs associated with it are information costs (often neglected in
neoclassical theorizing); it does not wear out when used.

Market process economics emphasizes that capital is a complex structure, involving
time and uncertainty, and it views markets as disequilibrium processes. The
capital goods we call software are especially heterogeneous; not only do different
programs accomplish entirely different tasks, but they are written in many different,
incompatible languages. The patterns of complementarity of software-capital
are exceptionally complex: most programs can run only on certain kinds of machines,
performing tasks useful only in certain kinds of endeavors. A given CAD-CAM
(computer aided design-computer assisted manufacture) system, for example, may
require a particular computer with particular specifications, and a particular
manufacturing device. In the software industry the detailed timing of
events is particularly important. Change is so rapid that a product not released
in time is doomed, and capital destruction, through the advance of knowledge,
occurs constantly. Old programs and machines created at great cost and bought
at great price become obsolete in months.

Conceiving
of software as capital goods calls into serious question the common treatment
of capital in economic models as accumulating solely out of saving.

In many growth models, for instance, new capital in a given period is defined
as the unconsumed product of the previous period. This model implies that the
most important thing new capital requires is previous physical output. It ignores
what the Austrian view emphasizes: the role of knowledge and creativity. Beer
barrels and blast furnaces are more than the saved wood and steel from a previous
period. They are additionally the embodiment of careful plans and much accumulated
knowledge. The new capital is a combination of the physical wood and steel with
this essential knowledge.

With software, the knowledge aspect of capital reaches its logical extreme.
Software has virtually no physical being. It is essentially a pattern of on
and off switches in the computer. As such, it is pure knowledge. No physical
output from a previous period is required to produce it, except perhaps for
the Coca-Cola (a.k.a. "programming fluid") and Twinkies the software
engineer consumes as he generates the code. The knowledge and creativity of
the programmer are crucial.

Other studies that fall within this research category include studies of the
evolution of standards and of the development of interoperability. What becomes
an industry standard is of immense importance in determining the path an industry
takes. As, say, some particular operating system appears to be outstripping
others in popularity, as MS-DOS did in the microcomputer industry, for example,
the first versions of new programs tend to be written for that operating system,
to improve their chances of acceptance. This is often the case whether or not
that operating system is really the best available for the program's purpose.
In this way, as software developers bet on an uncertain future, industry standards
develop. "Interoperability" means making it possible for software
that is built to one standard to be used with another standard via "software
adapters." With interoperability, now available in limited degree, software
developers have less at stake in using some less-preferred programming language
or operating system that better suits their needs. Decisions to choose on the
merits rather than by projections of others' preferences will tend to influence
the evolution of industry standards. What are the economic causes and consequences
of the evolution of a particular industry standard? What would be the economic
consequences of more widespread interoperability? These are interesting questions
worthy of research.

In sum, studying the computer industry seems likely to inform our understanding
of economic processes from a new, illuminating perspective.

2. Information Technology and the Evolution of Knowledge and
Discourse

Knowledge evolves, and media are important to the
evolution of knowledge. Hypertext publishing promises faster and less expensive
means for expressing new ideas, transmitting them to other people, and evaluating
them in a social context. Links, in particular, will enable critics to attach
their remarks to their targets, making criticism more effective by letting readers
see it. Hypertext publishing should bring emergent benefits in forming intellectual
communities, building consensus, and extending the range and efficiency of intellectual
effort.

--Drexler ( 1987, p. 16)

Both the Xanadu and AMIX groups pay much attention to evolutionary processes,
which is one reason Hayek's work is so attractive to them. They are particularly
interested in the evolution of knowledge. One of the crucial questions which
they focus on is the evolution of ideas in society. In this regard, the hypertext
publishing system under development at Xanadu is of great importance. It will,
they hope, provide a market for information of a scientific and scholarly kind
that will more closely approach the efficiency standards of our current markets
for goods and services.

The market for scholarly ideas is now badly compartmentalized, due to the
nature of our institutions for dispersing information. One important aspect
of the limitations on information dispersal is the one-way nature of references
in scholarly literature. Suppose Professor Mistaken writes a persuasive but
deeply flawed article. Suppose few see the flaws, while so many are persuaded
that a large supportive literature results. Anyone encountering a part of this
literature will see references to Mistaken's original article. References thus
go upstream towards original articles. But it may be that Mistaken's article
also provokes a devastating refutation by Professor Clearsighted. This refutation
may be of great interest to those who read Mistaken's original article, but
with our present technology of publishing ideas on paper, there is no way for
Mistaken's readers to be alerted to the debunking provided by Clearsighted.
The supportive literature following Mistaken will cite Mistaken but either ignore
Professor Clearsighted or minimize her refutations.

In a hypertext system such as that being developed at Xanadu, original work
may be linked downstream to subsequent articles and comments. In our example,
for instance, Professor Clearsighted can link her comments directly to Mistaken's
original article, so that readers of Mistaken's article may learn of the existence
of the refutation, and be able, at the touch of a button, to see it or an abstract
of it. The refutation by Clearsighted may similarly and easily be linked to
Mistaken's rejoinder, and indeed to the whole literature consequent on his original
article. Scholars investigating this area of thought in a hypertext system would
in the first place know that a controversy exists, and in the second place be
able to see both (or more) sides of it with ease. The improved cross-referencing
of, and access to, all sides of an issue should foster an improved evolution
of knowledge.

A potential problem with this system of multidirectional linking is that the
user may get buried underneath worthless "refutations" by crackpots.
The Xanadu system will include provisions for filtering systems whereby users
may choose their own criteria for the kinds of cross-references to be brought
to their attention. These devices would seem to overcome the possible problem
of having charlatans clutter the system with nonsense. In the first place, one
would have to pay a fee for each item published on the system. In the second
place, most users would choose to filter out comments that others had adjudged
valueless and comments by individuals with poor reputations.[1]
In other words, though anyone could publish at will on a hypertext system, if
one develops a bad reputation, very few will ever see his work.

Another difficulty of today's paper publishing environment is that turn-around
times are extensive. Mistaken's persuasive but flawed article can be the last
word on a subject for a year or so, before Clearsighted can get her responses
accepted in a journal and then published. Even then, many of those who have
read Mistaken may not have ready access to the journal in which Clearsighted
is published. In a hypertext publishing environment, by contrast, Clearsighted's
responses can be available literally within hours of the publication of Mistaken's
original article. Thus a hypertext system seems able to inhibit the spread of
bad ideas at their very roots. Refutations of bad ideas could immediately become
known, and unorthodox new ideas, if sensible, could more rapidly gain the support
of fair-minded thinkers.

The research program for economists that is suggested by hypertext publishing
falls within the field of the philosophy of science. It involves interpreting
and explaining the effects of technology on the shape of economic research in
the past, and projecting how advances such as hypertext might reshape it in
the future. In the early days of economics as a profession, book printing was
the leading edge of technology, and accordingly most economics consisted of
verbal arguments. In more recent years the advent of number-crunching computers
has enabled the development of more complex mathematical modeling and econometric
techniques. But today's microcomputers, through word processing, have also begun
to dramatically enhance economists' ability to present verbal arguments. (This
very sentence, for instance, was all but effortlessly inserted in the fourth
draft of this article, with no necessity for retyping or cutting and pasting.)
The advent of hypertext might revitalize the rich literary tradition of economics
in at least three ways. First, it would facilitate research by drastically reducing
time spent among library shelves. Second, it would enable easier reference to,
and more rapid dissemination of, verbal arguments. Third, it would provide a
medium through which any number of economists might easily carry on open-ended
written discussions with one another, with no inconvenience of time and distance.

3. Complexity, Coordination and the Evolution of Programming Practices

Two extreme forms of organization are the command economy and the market
economy. The former attempts to make economic tradeoffs in a rational, centrally-formulated
plan, and to implement that plan through detailed central direction of productive
activity. The latter allows economic tradeoffs to be made by local decision
makers, guided by price signals and constrained by general rules.

Should one expect markets to be applicable to processor time, memory space,
and computational services inside computers? Steel mills, farms, insurance
companies, software firms--even vending machines--all provide their goods
and services in a market context; a mechanism that spans so wide a range may
well be stretched further.

--Miller and Drexler (1988b, p. 137)

The type of research that would be most directly a follow-up of the paper "Markets
and Computation: Agoric Open Systems" by Miller and Drexler, would involve
examining the history, and the possible future, of computer programming practices
as illustrative of economic principles. Inside computers things are going on
which have some amazing similarities--and of course some significant differences--to
what goes on in human economies. Many of the problems that arise in human economic
systems have their analogs in well-known problems in computational systems.

Economics has been conceived by many of its practitioners (e.g., Ludwig Mises,
Lionel Robbins, Gary Becker) as applicable in general to the study of choice,
the making of decisions, the application of scarce means to valued ends. Programmers
are faced with difficult choices of how to make the best use of scarce computational
resources. F .A.Hayek has recast the economic problem in terms of how societies
may make effective use of the knowledge that is so widely dispersed among all
the people they comprise. Hayek has argued that coordination in complex systems
such as human economies exceeds the capabilities of central planning and direction.
Coordination is achievable in complex economies only through decentralized decision-making
processes: through specialization and the division of labor, through property
rights, and through the price system.

Programmers are now facing similar problems of complexity. As programs and
distributed computation systems grow larger, they are outrunning the capacity
of rational central planning. Coping with complexity seems to depend on decentralization
and on giving computational "objects" property rights in their data
and algorithms. Perhaps it will even come to depend on the use of price information
about resource need and availability that can emerge from competitive bidding
among those objects.

As the following paragraphs indicate, there is much that could be done in the
direction of elaborating on these analogies between economics and programming
practices, and using them to develop a better understanding of economics. It
doesn't matter where one stands on the question of how similar computational
and market processes are for one to see the possible value of this research
program. Even if the differences are enormous, explaining exactly what they
are could be exceedingly valuable to both fields.

The Division of Labor and Modularity

An appreciation of the advantages of the division of labor is embodied in the
programming principle of modularity. The earliest programs were linear, undivided
sequences of instructions, but with the evolution of programming, practical
considerations forced a dividing up of the problem into discrete modules. The
extensive use of subroutines and structured programming enhanced the ability
of programmers to solve their problems. They broke down the whole into manageable
chunks, as it were, whose activities were known and clearly bounded.

For the various subroutines to operate effectively, they need a certain amount
of autonomy--if other parts of the program interfere with them in unexpected
ways, the result is a crashed program or nonsense. Or, we might say, the subroutines'
"rights" to what is "theirs" need to be respected. The analogy
to property rights is very close.

Property Rights and Object-Oriented Programming

The practical advantages that property rights give the economy can be provided
for computer programs by what is called "object-oriented programming"
(the common acronym in the computer language literature is OOPS, for object-oriented
programming systems). In object-oriented programming, the different kinds of
tasks that the program must carry out are assigned to "objects," essentially
autonomous sections of code whose workings cannot be interfered with by other
parts of the program, because the boundaries between objects are clear and respected.
One subroutine's data cannot, "intentionally" or by accident, interfere
with data "belonging to" another subroutine. One advantage to the
programmer is that he need not hold in his mind all at once the myriad possible
options and combinations of calculations and data. The programmer need not know
how an object works, only what it does. Another advantage is that if an object
is directed to do something it cannot do, it simply returns a message that it
"does not understand," instead of acting on the bad instructions in
a senseless or destructive way. The program's "labor" is thus not
only divided up among many parts, but the "rights" of these parts
are respected.

The integrity of the various data structures and algorithms in OOPS provides
especially welcome clarity in very large programs written by teams of programmers.
In this setting the likelihood of mutual interference would be very great--analogous
to the tragedy of the commons--without the "property rights" structure
of OOPS.

The Use of Knowledge in Computation

At one point Roger Gregory, a founder of Xanadu, made a comment which helped
us understand why his group is so interested in market process economics. The
reason is that programmers, in their day-to-day experience, cannot help but
learn the severe difficulties in getting large, centrally planned systems to
work properly. The bigger and more complex their own programs, the more difficulty
they have with bugs. Virtually never does a large program work as intended the
first time, even though the programmer has absolute control over every aspect
of the system. Programmers spend many frustrated hours debugging their own work.
Accordingly, they tend to be very dubious about the ability of government planners
to develop successful systems in human society, where the complexity is far
greater and the ability to control is far less.

What this suggests of course, is Hayek's line of thought that the more complex
a system is, the more necessary it becomes that the orderliness of the system
grow out of the interaction of relatively autonomous parts. The nature of complexity
is arguably the central issue in computer science today, as it is in Hayekian
economics. As Mises and Hayek have emphasized, the reason that central planning
of economies does not and cannot work is that human society is too complex.
Programmers are beginning to realize that "central planning" of computational
systems is fraught with the same difficulties.

So far we have been describing economic insights that are already widely appreciated
by computer scientists, albeit not in the same terms economists use. The most
innovative aspect ofMiller and Drexler's papers is their introduction of "agoric
systems." Derived from the Greek word for marketplace, agoric systems aim
at solving the problem of maintaining coordination in complex computational
systems by the same means as in complex economies: by a price system.

As Miller and Drexler (1988b, p. 163) put it:

Experience in human society and abstract analysis in economics both indicate
that market mechanisms and price systems can be surprisingly effective in
coordinating actions in complex systems. They integrate knowledge from diverse
sources; they are robust in the face of experimentation; they encourage cooperative
relationships; and they are inherently parallel in operation. All these properties
are of value not just in society, but in computational systems: markets are
an abstraction that need not be limited to societies of talking primates.

Miller and Drexler are concerned about the efficiency of computer resource
use, both within particular programs running on single computers and across
extended computational networks over large geographical areas. In both cases
they envision allocation of scarce computational resources such as disk or memory
space and processor time--being determined by a market process among computational
objects. As in the economy, it is not enough that agents have property rights;
it is necessary also that they be able to communicate their special knowledge
of time and place.

Traditional programming practices have been based on a central planning approach,
deliberately deciding on tradeoffs, such as that between the speed at which
the program completes its task with the core space it takes to do so. Computer
time has traditionally been allocated on a time-share or first-come-first-served
basis, or by some other fixed prioritizing system.

Miller and
Drexler wish to persuade the computer community to drop this central planning
model for allocating computational resources. Instead, programs should be designed
so that their different parts would "bid competitively" for, say,
the "rental" of memory space, which would be more expensive per millisecond
than disk space, just as downtown property rents at a higher rate than rural
land. Likewise, in large, distributed systems, the various firms, individuals,
research centers and so on would bid for the computational goods they need.
Presumably this bidding and asking would be carried out by the computers themselves,
according to pre-programmed instructions. We might find certain computational
resources in extremely high demand, or in volatile markets, changing their prices
several times a day--or several times a second.

Imagining Computation Markets of the Future

Miller and Drexler envision the evolution of what they call agoric open systems--extensive
networks of computer resources interacting according to market signals. Within
vast computational networks, the complexity of resource allocation problems
would grow without limit. Not only would a price system be indispensible to
the efficient allocation of resources within such networks, but it would also
facilitate the discovery of new knowledge and the development of new resources.
Such open systems, free of the encumbrances of central planners, would most
likely evolve swiftly and in unexpected ways. Given secure property rights and
price information to indicate profit opportunities, entrepreneurs could be expected
to develop and market new software and information services quite rapidly.

Secure property rights are essential. Owners of computational resources, such
as agents containing algorithms, need to be able to sell the services of their
agents without having the algorithm itself be copyable. The challenge here is
to develop secure operating systems. Suppose, for example, that a researcher
at George Mason University wanted to purchase the use of a proprietary data
set from Alpha Data Corporation and massage that data with proprietary algorithms
marketed by Beta Statistical Services, on a superfast computer owned by Gamma
Processing Services. The operating system needs to assure that Alpha cannot
steal Beta's algorithms, that Beta cannot steal Alpha's data set, and that neither
Gamma or the George Mason researcher can steal either. These firms would thus
under-produce their services if they feared that their products could be easily
copied by any who used them.

In their articles, Miller and Drexler propose a number of ways in which this
problem might be overcome. In independent work, part of the problem apparently
has already been overcome. Norm Hardy, senior scientist of Key Logic Corporation,
whom we met at Xanadu, has developed an operating system caned KeyKOS which
accomplishes what many suspected to be impossible: it assures by some technical
means (itself an important patented invention) the integrity of computational
resources in an open, interconnected system. To return to the above example,
the system in effect would create a virtual black box in Gamma's computer, in
which Alpha's data and Beta's algorithms are combined. The box is inaccessible
to anyone, and it self-destructs once the desired results have been forwarded
to the George Mason researcher.

In the sort of agoric open systems envisioned by Miller and Drexler, there
would be a vigorous market for computational resources, which could be sold
on a per-use basis, given a secure operating system. Royalties would be paid
to the owners of given objects, which might be used in a variety of applications.

Programmers would be able to develop software by adding their own algorithms
onto existing algorithms. They would not need to understand all the workings
of what they use, only the results. Among other advantages, this would save
the tremendous amount of time now used by programmers in the trivial redevelopment
of capabilities that have already been well worked out. Most important, however,
is the increased rapidity with which new products could be developed.

4. Mind as a Spontaneous Order: What is (Artificial) Intelligence?

If multilayered networks succeed in fulfilling their promise, researchers
will have to give up the conviction of Descartes, Husserl, and early Wittgenstein
that the only way to produce intelligent behavior is to mirror the world with
a formal theory in the mind. Worse, one may have to give up the more basic
intuition at the source of philosophy that there must be a theory of every
aspect of reality--that is, there must be elements and principles in terms
of which one can account for the intellegibility of any domain. Neural networks
may show that Heidegger, later Wittgenstein, and Rosenblatt were right in
thinking that we behave intelligently in the world without having a theory
of that world.

--Dreyfus and Dreyfus (1989, p. 35)

The field of computer science that market process economists would be apt to
find the most fascinating is Artificial Intelligence (AI). The traditional approaches
to AI, still dominant in the specialization area known as "Expert Systems,"
takes intelligence to be an algorithmic, mechanical process. Although there
are many commercially successful applications of these traditional AI systems,
they have been extremely disappointing in terms of their ability to exhibit
anything that deserves the name "intelligence." Indeed, precisely
the aspects of intelligence that market process economists consider the most
important, such as learning, creativity, and imagination, have proven to be
the most difficult to produce artificially.

Over the past decade, however, a revolution has been occurring in AI researchers'
thinking about thinking. The newer approaches, sometimes called Emergent AI,
conceive of mental processes as complex, spontaneous ordering processes. Emergent
AI traces its origins to early contributions to neural networks such as those
of Donald 0. Hebb (1949), whom Hayek cites favorably in The Sensory Order, and
Frank Rosenblatt (1958; 1962). These efforts had at one time been discredited
by the more rationalistic approaches, but they are today making a dramatic comeback.
As Sherry Turkle (1989, pp. 247-8) put it in an article contrasting "the
two AIs":

Emergent Al has not been inspired by the orderly terrain of logic. The ideas
about machine intelligence that it puts forward are not so much about teaching
the computer as about allowing the machine to learn. This AI does not suggest
that the computer be given rules to follow but tries to set up a system of
independent elements within a computer from whose interactions intelligence
is expected to emerge.

The critiques in the new AI literature of the failings of the rationalist approach
to AI sound remarkably similar to Hayekian economists' critiques of the rational
choice model. Even the very same philosophical traditions market process economists
have used--post-Kuhnian philosophy of science, the later Wittgenstein, and contemporary
phenomenology and hermeneutics--have been used by the newer AI researchers as
the basis of their critique of Cartesian rationalism.

A great deal of work has been done in the new Emergent AI literature that simulates
complex ordering processes. For example, there are the "genetic algorithms"
and classifier systems approaches developed by John Holland, and the connectionist
or neural networks approaches. A significant faction of the AI community thus
finds itself arriving at essentially the same conclusions about the nature of
the human mind as Austrian economists. The process of mind is a decentralized,
competitive process. No CPU exists in the brain. Marvin Minsky's conception
of the way the mind works in The Society of Mind is remarkably similar to Hayek's
in The Sensory Order. Hayek draws significant methodological conclusions from
his view of the nature of the mind, for example in his essays "Rules, Perception
and Intelligibility" and "The Primacy of the Abstract."

Some of the interesting work that has been done within Al directly introduces
market principles into the design of models of learning. Some initial research
along these lines has been done by Ted Kaehler and Hadon Nash at Apple, in a
system they named Derby. This approach tries to introduce market bidding processes
and monetary cost calculations into a neural network model in order to generate
artificial learning. In his work on classifier systems, John Holland makes explicit
use of market processes for reinforcing a program's successful rules of action.
"We use competition as the vehicle for credit assignment," he says.
"To do this we treat each rule as a 'middleman' in a complex economy"
(1986, p. 380). He speaks of suppliers, consumers, capital, bids, and payments.

This type of research involves using economic principles to advance artificial
intelligence research, but our interest in it is not primarily altruistic; the
point is not so much to help Al achieve its aspirations, but to see what it
can teach us economists about the nature of human intelligence.

It
is too early to tell whether these approaches to Al will be more successful
in replicating learning, creativity, inductive thinking, and so forth than the
traditional approaches, but it is already clear that the Emergent Al approach
is able to do some things the older approaches couldn't. Even if one believes
many current approaches to Al are utterly unworkable, their very failures might
be worthy of closer study. The fact that mainstream Al research has not yet
been able to reproduce certain key aspects of human intelligence may be highly
significant. Books critical of mainstream Al, such as Hubert Dreyfus's What
Computers Can't Do, and Terry Winograd and Fernando Flores's Understanding
Computers and Cognition, present a powerful critique of the rational choice
model, one from which we could borrow in our own efforts to challenge mainstream
economics.

Understanding how the human mind works is not only of interest in that the
mind is conceived as a specific instance of spontaneous order processes, and
as such may display some interesting analogies to market orders. There is also
the point that, for a subjectivist approach to economics, what the market order
is, is an interplay of purposeful human minds. We need to know as much as we
can about how human beings think and communicate for our substantive economics.
Al, in its failures and its achievements, is wonderfully informative on these
issues.

Even the skeptic about Al research and computer modeling in general could see
that these simulation methods raise some provocative research questions for
market process economics. To what extent have genuine learning and creativity
been simulated in this research? How much does it matter that natural language
simulation has not yet gotten very far? Just how different are these different
levels of spontaneous order, as we advance from biological to animal and human
cognitive processes, and to market processes, and what properties do they share?

Underlying our approach to this subject is our conviction that "computer
science" is not a science and that its significance has little to do
with computers. The computer revolution is a revolution in the way we think
and in the way we express what we think. The essence of this change is the
emergence of what might best be called procedural epistemology--the study
of the structure of knowledge from an imperative point of view, as opposed
to the more declarative point of view taken by classical mathematical subjects.
Mathematics provides a framework for dealing precisely with notions of "what
is." Computation provides a framework for dealing precisely with notions
of "how to."

--Abelson and Sussman (1985, p. xvi)

Perhaps the research direction we talked about which will prove to be most
controversial involves not so much using economics to study computers as the
other way around, the direct use of computer modeling techniques to develop
economic theory. This would be a matter of expanding on ideas sketched in another
of Miller and Drexler's papers, "Comparative Ecology: A Computational Perspective."
Up to now we have been talking about applying more or less standard market process
economics to some computer-oriented topics. Those first topics could be done
in words, as it were, but this one could also involve actually doing some computer
programming of our own.[2] The idea here is that we could try to improve our
understanding of the market order by developing spontaneous order simulations
on a computer. We might be able at least to illuminate existing ideas in market
process economics, and we might conceivably develop substantive new ones, by
doing mental experiments within artificial minds.

How, it might be asked, could a school which stresses the non-mechanical nature
of human action find itself trying to simulate action on electronic machines?
After all, market process economists think an alternative approach to the subject
matter is necessary precisely because neoclassical economics has tried to subsume
action into a mechanistic model.

But what we mean by "mechanical" has been shaped by the age when
machines were extremely crude and rigid things. As computers advance, they increasingly
challenge our ideas about what "machines" are capable of. In principle,
there is no reason why computers themselves could not become sufficiently "non-mechanistic"
to be of interest to market process economics. As the previous section pointed
out, research in Artificial Intelligence aspires to reproducing on electronic
machines exactly the sorts of phenomena, such as creativity and learning from
experience, in which market process economics is interested.

The market process approach has never been against models as such, but has
only objected to modeling that loses sight of certain non-mechanical aspects
of human choice. If the aspects of human action that the school considers the
most important cannot be handled with the modeling tools of the mainstream,
it may be necessary to devise better tools. We have to admit at this point that
we are not at all sure ourselves whether market process economics can usefully
deploy computer simulation methods. But the only way to tell if computer simulations
can serve as such tools would be to try to build some, and see what can be done
with them.

Market process oriented economists have often pointed out that the mathematics
of differential calculus that has played such a central role in mainstream economics
is not the appropriate mathematics for studying the economy. James Buchanan,
for example, has suggested that game theory, since it can deal with the interplay
of strategic choices through time, would constitute a more appropriate mathematics.
Kenneth Boulding has pointed to topology as an alternative mathematics, because
it can deal with shapes rather than quantities. Without disagreeing with the
reasons game theory and topology might be useful formalizations, we suspect
that the appropriate formalization for economics might not be a mathematics
at all. Computer programming may constitute the kind of formalization most conducive
to the development of market process theory.[2] It is a formal medium for articulating
the "how to" of dynamic processes, rather the "what is"
of timeless end-states with which mathematics is concerned. Mathematical modeling
has often distracted economics from paying attention to the processes that market
process economics emphasizes. Computer "simulations" of spontaneous
order processes might prove to be the kind of modeling approach that is process-oriented
enough to help rather than obstruct economic theorizing. Thus it could constitute
a useful new complement to the traditional procedures of theorizing that market
process economists now employ.

It is important to be clear about just what is meant by "simulation"
here. It certainly has nothing to do with efforts to build direct simulations
of specific real world markets, or of the overall economy. The "worlds"
in the computer would be radically simplified, and the "agents" would
be only "artificially" intelligent, which is to say, at this point
in AI research, they would be rather stupid. But these agents may still be more
like humans than the optimizing agent of mainstream neoclassical theorizing:
they may be equipped with the capacity to learn from experience. But there would
be no pretensions of capturing the complexities of reality within a model, or
of being able to derive predictions about reality directly from the simulation
exercises. The notion here is rather of using computers as an aid for conducting
complicated mental experiments. It would not be empirical but theoretical research.

On the other hand, it would be more experimental, in a sense, than most theoretical
research is today. This kind of simulation would differ from most contemporary
theorizing, in that the purpose of the modeling exercise would not be to devise
a whole deterministic mechanism, such as is the goal of IS/LM and Rational Expectations
models in macroeconomics, or general equilibrium theory in microeconomics. Rather,
the aim would be to set up constraining conditions, specifying institutional
environments or decision rules for agents, and then to run the simulation in
order to see what happens. The idea is not to create a mathematical model that
already implies its conclusions in its premises. Rather, it is to run the simulations
as mental experiments, where what is of interest is not what the end results
are so much as how the process works. And we, the programmers, would not know
how the process was going to come out until we ran the mental experiments. The
order would emerge not by the programmer's design, but by the spontaneous interplay
of its component parts.

One of the first projects that needs to be undertaken is to see just how much
there is in existing computer modeling, inside or outside of economics, which
we might critically examine as illuminating the properties of spontaneous order
processes. The various evolutionary process modeling strategies mentioned in
the previous section, that are being used in Artificial Intelligence, in theoretical
evolutionary biology, and in theoretical ecology, could be reinterpreted as
referring to economic institutions or economies instead of brains or species.
Existing program designs could be examined from a Hayekian perspective and modified
in order to illuminate selected properties of spontaneous order processes. Or
completely new programs could be developed with markets more directly in mind.

Imagine, for example, trying to contrive Mengerian simulations in which a medium
of exchange spontaneously evolves, or free banking simulations that evolve clearinghouses
and stable monies. In industrial organization, it might be possible to investigate
how firm sizes vary with different industry characteristics, and how industries
evolve as markets and technology change. Business cycle effects might be studied:
could we, for example, observe changes in a simulated capital structure as a
result of an injection of credit? We might probe constitutional economics by
running a series of parallel simulations differing only in certain fundamental
rules such as property rights and contract sanctity. What different emerging
properties of the economic order would we observe?

Of course we need to be careful about the unrealistic nature of these mental
experiments, and not let the modeling become an end in itself. The crippling
vice of most economic theory today is its "model-fetishism." Economists
get preoccupied with the game of modeling itself, and forget the need to interpret
the mental experiment. Although most game theory in economics suffers as much
from formalism as general equilibrium theory and macroeconomics do, the iterative
game theory work of Robert Axelrod is very much the kind of research we are
thinking of here. There, the computer tournament was couched in a substantive
interpretive effort. The mental experiment was not just a game played for its
own sake, but a heuristic vehicle, a mental experiment to help us to think about
the evolution of human cooperation.

Other
than Axelrod's work, probably the closest thing in the existing economics literature
to this kind of simulation would be what is called experimental economics. Whereas
our simulations would not use human subjects, as does most economic experimentation,
the experimental economists' focus on the design and functioning of market institutions
is very much in the spirit of what we have in mind. Moreover, the use of non-human
agents would in many instances allow for greater flexibility in the design of
spontaneous order simulations. lnstead of using rats, we could use artificial
agents. As Ted Kaehler pointed out, this is a step down in many dimensions of
intelligence, but there are other advantages of computer experiments which suggest
that some interesting theory development might be possible along these lines.
The more Hayekian contributors to the experimental literature, such as Vernon
Smith and Ron Heiner, will undoubtedly have many useful ideas for us along these
lines, but otherwise we believe this kind of theoretical spontaneous order simulation
is unexplored territory for market process economics.

Miller, Drexler, and Salin deserve our thanks for introducing Hayekian ideas
to the computer science community, and we certainly encourage computer scientists
to follow up directly on their work. Conceivably, we economists might be able
to introduce theoretical principles to computer scientists that could help them
address their problems. But a more likely benefit of our taking up these questions
is that, by applying our existing economics to the study of computational processes,
we might help improve our economics, and that may help us think more clearly
about human economies, which is, after all, what we are really interested in.

End Notes

[1]Plans for Xanadu also include provisions for reader feedback as to the value
of different articles or notes, and for royalty payments per use to all authors,
even of short comments.

[2]This is not necessarily
as radical a change from writing "in words" as we are implying here.
Computer programs are written in order to be read by people, and not merely
to be run on machines. As Abelson and Sussman (1985, p. xv) put it in their
classic textbook on programing, a computer program is not just a way of getting
a computer to do something, it is "a novel formal medium for expressing
ideas about methodology. Thus, programs must be written for people to read,
and only incidentally for machines to execute."

Appendix: Annotated Bibliography

The interface of computer science and market process economics provides a wealth
of exciting research opportunities. By studying both the application of market
process ideas to computer science and the ways in which developments in computer
science enrich our understanding of market processes, we can expand the interplay
between theoretical insights and empirical praxis. In this bibliographical note,
we hope to alert interested readers to some of these sources. While this list
is far from exhaustive, we hope that it will provide a useful starting point
for those wishing to pursue these research opportunities.

Process-Oriented Case-Studies of the Computer Industry

The computer industry, with its fast growth and rapid technological change,
provides fertile soil for studying such traditional market process concerns
as the role of entrepreneurship (Kirzner, 1973), the production and use of knowledge
and information (Hayek, 1948), and the peculiarities of software as capital
(Lachmann,1978). The computer industry, however, is not only an increasingly
important sector in its own right, but also is one that is ushering in new forms
of market interaction in a broad variety of industries. The emergence of electronic
markets (Malone, et at., 1989) in such areas as computerized airline registration
systems, program trading on increasingly interlinked electronic exchanges, as
well as computer-aided buying and selling through home-shopping systems, has
become an important research and policy topic.

More broadly, the rapid evolution of information and communication technologies
and the peculiarities of information as a good underscores the important relationship
between the legal environment and technical change (Liggio and Palmer, 1988).
The interplay of technological, contractual, common-law, and legislative solutions
to such problems as the assignment of property rights to intangible goods, for
example the broadcast spectrum and intellectual property (Palmer, 1988), is
an exciting and relatively unexplored research opportunity.

In addition, the blurring of the distinctions between the various communications
media (print, broadcasting and common carriers), as evidenced by the recent
breakup of AT&T, highlights the relationship between innovation, public
policy, and market competition (Pool, 1982, Huber, 1987, Mink and Ellig, 1989).

A further example of the important dynamic interactions between technical and
policy responses to change in the computer industry can be found in the emergence
of industry standards, whether as the outcome of the market process, or imposed
by legislation, or through agreement forged in standard committees (Katz and
Shapiro, 1985, David, 1987). The problems of compatibility and interoperability
between various software and hardware components highlights the role of market
dialogue in the shaping of expectations and the formation of consensus in reaching
an industry standard. In addition, the process of standard formation is constantly
being threatened by the entrepreneurial discovery process (Hayek, 1978a) leading
to the search for technical adapters and innovations which would make such standards
obsolete. Moreover, this process may exhibit a high degree of historical path
dependency, as the dominance of the technologically inferior QWERTY keyboard
demonstrates (David, 1986, Could, 1987).

An additional area of interest to market process economists is the role that
computers have played in the socialist calculation debate. For many theorists,
the computer was seen as the answer to the challenge of Mises and Hayek to the
workability of central planning (Lange, 1967). In light of recent trends in
computer science towards decentralized means of coordination, the bold claims
of future computopians takes on an ironic twist (Lavoie, 1990). A recent attempt
to implement a version of a computopian plan in Chile (Beer, 1975) could be
usefully examined by market process economists.

Information Technology and the Evolution of Knowledge and Discourse

While it is commonplace to hear how computers will transform our society, the
reality seems to move much slower than the promised dramatic effects. Nevertheless,
as computers become more widespread and interconnected, the use of computer-aided
dialogue and communication could have important repercussions on the evolution
of knowledge and scientific discourse (Bush, 1945). These new knowledge media
(Stefik, 1988) could have an effect not seen since Gutenberg.

As Hypertext systems emerge (Nelson, 1973, Drexler, 1986, 1987), they could
enhance the evolution of scientific knowledge through more rapid dissemination
of knowledge, and more efficient means of criticism and debate. These developments
could have important implications for the spread of economic ideas, and the
pattern of discourse within economics (Tullock, 1965; McCloskey, 1985; Colander
and Coats, 1989).

Complexity, Coordination and the Evolution of Programming Practices

As software applications become more advanced, and computer systems more powerful,
a central problem that emerges is how to cope with the rapidly expanding complexity
of software systems. In computer systems the knowledge embodied in the software
is more important than the physical hardware upon which it runs. As Alan Kay
expresses it, "architecture dominates material" (Kay, 1984). Computer
programming is properly seen as a medium of expression of ideas, a product of
the human mind (Ableson and Sussman, 1985). The techniques used to grapple with
the intellectual complexity of large software systems (Brooks, 1975) could be
usefully studied in comparison to techniques in economics. While the complexity
of an economic system is obviously much greater than even the most complex software
system (Hayek, 1967b), the methods used to maintain intellectual coherence,
and to improve the use of knowledge may have analogs in each system. Such issues
and techniques as modularity, specialization, embodied knowledge, and use of
local knowledge have their counterparts in each system.

Miller and Drexler have usefully applied the analogy between economic institutions
and programming practices through their insight that the latest development
in programming languages (Object Oriented Programming) can be viewed as being
the reinvention of the concept of property rights in the domain of computer
science (Miller and Drexler, 1988b). Insights from the economics of property
rights could perhaps be usefully applied to the development of software systems
(Demsetz, 1967, Anderson and Hill, 1975, Hayek, 1989).

Object oriented programming techniques such as encapsulation that allows for
the separation of internal state from external behavior, as well as the coupling
of data and procedures, promise to expand the range of a programmer's conceptual
control. Object oriented programs perform computations by passing messages between
various objects, which can be viewed in terms of their real-world analog (Thomas,
1989; Cox, 1986; Shriver and Wegner, 1988).

While current software systems can be very complex, their complexity is sure
to increase as computation becomes more distributed across networks of heterogeneous
computers with different users pursuing their own particular goals. As Miller
and Drexler point out, central planning techniques are no longer adequate for
coordination of these open systems, and a more decentralized coordinating mechanism
is needed (Hewett, 1985; Bond and Glassner, 1988; Kahn and Miller, 1988; Huberman,
1988). An important aspect of these open, decentralized systems will be the
need to maintain the security of the proprietary data and software of the different
agents (Miller, et al., 1987; Hardy, 1988). An additional aspect of the combination
of large distributed systems and object oriented programming is the promise
it holds for the more rapid evolution of software applications. (Miller and
Drexler, 1988b; Drexler, 1988, 1989). Agoric open system can take advantage
of object oriented programming's ability to provide opportunities for easy reuse
and recombination of components, and incremental improvements.

Mind as a Spontaneous Order: What is (Artificial) Intelligence?

A further area of interest for market process economists refers to the insights
regarding the nature of rationality that have been achieved through the attempt
to create artificially intelligent computers. While these attempts have yielded
many interesting applications, they have had little success in creating anything
resembling intelligence. However, much has been learned by the attempt. Economists
have much to gain from the successes and failures of artificial intelligence.
The work of Herbert Simon, of course, has been influential in both economics
and computer science (Newell and Simon, 1972; Simon, 1983, 1985), and has been
instrumental in bringing to the attention of economists the nature of the computational
demands placed upon their perfectly optimizing agents. The inability of humans
to fulfill the demands of the optimizing agents has become increasingly recognized
(Kahneman, et al., 1982) as well as the implications that these less than perfect
agents have for economic theory (Heiner, 1983).

The limitations of attempting to design intelligence as a mechanistic, decision-making
process has led to a shift towards a view of intelligence as being an emergent
property of a complex learning process (Graubard, 1989; Drexler, 1989). The
mind is seen as a spontaneous order process in which the resulting intelligence
is greater than is possible by design (Hayek, 1952). The mind is viewed as being
subject to competitive and cooperative pressures like other complex, evolving
systems. A variety of metaphors have been explored in attempting to create an
emergent approach to artificial intelligence. Perhaps the best known are the
connectionist or neural network approaches, which attempt to mimic the neural
process of the brain itself. A wide variety of connectionist approaches are
currently being attempted (Hillis, 1985; McClelland and Rumelhart, 1986; Edelman,
1987; Cowan and Sharp, 1989; Schwartz, 1989; Reeke and Edelman, 1989), including
an attempt that applies some of the agoric insights to the problem of attributing
success to various "neurons" (Kaehler, et al., 1988).

In addition, to the neural metaphor, computer scientists have attempted to
apply social metaphors, recognizing the greater social intelligence (Lavoie,
1985) that emerges out of the interaction of less intelligent parts (Kornfield
and Hewett, 1981; Minsky, 1986; Campbell, 1989).

Genetic and evolutionary analogies from biology have also been influential
(Langton,1987). These approaches include the Eurisko project of Doug Lenat (Lenat,
1983; Lenat and Brown, 1988), and the genetic algorithm approach pioneered by
John Holland (Holland, 1975; De Jong, 1988; Goldberg, 1989; Booker et al. ,
1989). In addition the classifier system, also pioneered by John Holland, has
attempted to build a parallel rule based learning system that combines the rule
discovery properties of genetic algorithms, and an economic model for the problem
of credit assignment (Holland, 1986; Holland et al. , 1986; Booker, et al.,
1989).

These developments in computer science may improve our understanding of both
a wide variety of spontaneous order processes, as well as the nature of intelligence.
The failure of traditional Cartesian rationalist approaches to artificial intelligence
has prompted a critique similar to much of the market process economists' critique
of neoclassical rationality (Dreyfus, 1972; Dreyfus and Dreyfus, 1985, 1986,
1989; Winograd and Flores, 1986). These critiques have emphasized the important
role that language and social interaction play in intelligence, as well as the
limitations of the knowledge as representation approach (Dascal, 1989), in capturing
the tacit and context-dependent nature of knowledge (Polanyi, 1962).

Computation as a Discovery Procedure: Possibilities of Agoric Mental Experiments

The advances in artificial intelligence and computer programming suggest that
these techniques could be usefully applied to experimental modeling the complex
processes of interaction that occur in economic systems. The goal of this type
of modeling is not a predictive model that tries to simulate reality, but rather
mental experiments to help us better understand spontaneous order processes.

One of the closest examples to the approach being suggested here is the work
of Robert Axelrod on the evolution of cooperation. Axelrod's mixture of theoretical
insights, a computer tournament, and empirical case studies has proved to be
both influential and illuminating (Axelrod, 1984). His work has inspired a wide
range of further empirical work and theoretical insights (Axelrod and Dion,
1988), including the use of genetic algorithms to generate strategies that in
certain situations improved on the performance of "tit for tat" (Axelrod,
1987 , J. Miller, 1989).

Another area that highlights the importance of the dialogic interplay between
theory and empirical observation is the fast-growing field of experimental economics
(Plott, 1982, Smith, 1982). While the majority of experiments to date have focused
primarily on the relatively straightforward auction-type institutions, the experimental
results have demonstrated the importance played by the exchange institutions--the
system of property rights in communication and exchange. As Vernon Smith notes,
"it is not possible to design a resource allocation experiment without
designing an institution in all of its detail" (1982, p. 923). This detailed
focus on the institutional constraints is perhaps the most valuable aspect of
the experimental approach. The focus to date in this young field has been on
relatively simple market institutions, and on static outcomes, not on the dynamic
adjustment and learning processes (Heiner, 1985). The complexity of keeping
track of these adjustment processes suggest a fruitful role for computers. Computer-aided
experimental markets, such as the computerized double auction mechanism (PLATO),
have already helped to illuminate these dynamic processes (Smith, et at. , 1988).
Furthermore, a group at the Sante Fe Institute has already combined Axelrod's
computer tournament model with the experimental focus on market institutions,
by running a computerized double auction tournament (Rust, et at., 1989).

Motivating much of this interest in computer modeling of spontaneous order
processes is a dissatisfaction with traditional equilibrium approaches to capturing
the evolutionary and self-ordering aspects of the market process. The development
of order analysis, as an alternative to equilibrium-bound theorizing can be
enhanced by our better understanding the working of spontaneous order processes
(Hayek, 1973, 1989; Buchanan, 1982; Boettke, et at., 1986; Horwitz, 1989), and
the nature of the rule systems and institutional order that help guide the ordering
processes (Brennan and Buchanan, 1985; Buchanan, 1986; Langlois, 1986). This
increasing interest in evolutionary approaches to economics (Nelson and Winter,
1982; Anderson, et at., 1988; Day, 1987; Allen, 1988; Silverberg, 1988; Holland,
1988) has been fueled in part by the developments in the new views of science
(Lavoie, 1989; Prigogine and Stengers, 1984).

Further work in developing "agoric mental experiments" can begin
by examining the current work in computer science that uses market principles.
Bernardo Huberman and his coworkers at Xerox PARC, building on the work of Drexler
and Miller (1988), have developed a computerized market allocation mechanism
called Spawn (Waldspurger, et at., 1989), and have explored the dynamic properties
of distributed computer systems (Huberman, 1988, 1989a,b; Huberman and Hogg,
1988; Huberman and Lumer, 1989; Kephart, et at., 1989a,b; Cecatto and Huberman,
1989). Market-based models for computation have also been explored by Tom Malone
at MIT (Malone, 1988; Malone, et at., 1988), and Ferguson (1989). Nascent attempts
to apply these computer techniques to economics have been attempted by members
of the Sante Fe Institute (Marimon, et at., 1989), and by the Agoric research
group at George Mason University (De Garis and Lavoie, 1989).

David, Paul. 1986. "Understanding the Economics of QWERTY: the Necessity
of History," in W. Parker, ed., Economic History and the Modern Economist
(Oxford: Basil Blackwell).

.1987. "Some New Standards for the Economic of Standardization in the
Information Age," in P. Dasgupta and Stoneman, eds., Economic Policy and
Technological Performance (Cambridge: Cambridge University Press).

.1978a. "Competition as a Discovery Procedure," in New Studies in
Politics, Philosophy, Economics and the History of Ideas (Chicago: University
ofChicago Press).

.1978b. "The Primacy of the Abstract," in New Studies in Politics,
Philosophy, Economics and the History of Ideas (Chicago: University of Chicago
Press). .1989. The Fatal Conceit: The Errors of Socialism (Chicago: Universityof
Chicago Press).

Don Lavoie is an associate professor of eonomics at George Mason University,
Fairfax, Virginia, and editor of Market Process. Howard Baetjer and William
Tulloh are both graduate students at George Mason University and fellows of
the Center for the Study of Market Processes.

Further Readings in Evolutionary Economics and Agoric Systems

A collection of papers from a meeting of the International Schumpeter Society
has been published as Evolutionary Economics: Applications of Schumpeter's Ideas
(Cambridge University Press 1988). Edited by Horst Hanusch, this collection
includes articles by Wolfgang F. Stolper, Burton H. Klein, Frederic M. Scherer,
Gunnar Eliasson, and Mark Perlman, among many others. Interestingly enough,
many modern-day evolutionary economists struggle, as Schumpeter did, with the
dynamic theory of creative destruction on the one hand, and the statics of formal
economic theory on the other. If you are looking for a solution to this apparent
dilemma, look elsewhere.

Economics has witnessed in recent years a revitalization of interest in evolutionary
approaches as an alternative to the standard equilibrium approach to the study
of complex phenomena. In a recently published paper, "The Evolution of
Economic Institutions as a Propagation Process" (Public Choice, 62, 1989,
pp. 155-172), Ulrich Witt expands on the game theoretic approach to the emergence
of institutions. Witt contrasts two forms of institutional evolution-spontaneous
emergence and collective action. Interested scholars can turn to Witt's book,
forthcoming in 1990, Individualistic Foundations of Evolutionary Economics,
(Cambridge: Cambridge University Press) for further developments of an approach
to the emergence and diffusion of innovations and the evolution of institutions.

The renewed interest in evolutionary approaches to economics has been fueled
in part by the recent developments in nonlinear dynamics in the natural sciences.
Brian Arthur, in his paper "Self-Reinforcing Mechanisms in Economics"
(in Philip W. Anderson, et al., eds. The Economy as an Evolving Complex System,
Redwood City, CA: Addison-Wesley Publishing Co., 1988, pp.9-31) examines several
sources of positive feedback or increasing returns in economics. In his paper
"Competing Technologies, Increasing Returns, and Lock-in by Historical
Events" The Economic Journal, March, pp. 116-131), he looks at one of these
sources, learning effects resulting from the adoption of a competing technology.
Arthur notes the importance of otherwise insignificant historical events in
giving an initial advantage to a certain technology, which is reinforced by
the positive feedback from the learning effects, locking-out other competing
technologies.

The possibility of historical path dependency in the adoption of technology
highlights the importance of empirical examinations of technological development.
George Basalla's book, The Evolution of Technology (Cambridge: Cambridge University
Press, 1989), provides a good place to start.

Ronald Heiner also examines the importance of nonlinear dynamic processes in
economics in his paper "The Origin of Predictable Dynamic Behavior"
(Journal of Economic Behavior and Organization, October, 1989). Heiner expands
on his research into the origin of predictable behavior by showing that regularity
in behavior may emerge as the result of agents' responses to nonlinearities
resulting from the fact that current preferences are partially dependent on
past experiences.

Michael T. Hannan and John Freeman, in their book Organizational Ecology (Cambridge,
MA: Harvard University Press, 1989), explore ecological and evolutionary models
of organizational change. Their emphasis on the dynamics of organizational change
should prove of particular interest to industrial organization economists.